Employment Effects of Innovation Activities

Abstract

The question of how technological progress affects the employment situation is an old one and has long been the focus of theoretical and empirical industrial organisation research as well as lively public discussions.33 The controversial debates on this issue mainly result from the fact that, from a theoretical point of view, different channels exist through which innovations can destroy existing jobs (displacement effects) but that there are also several mechanisms through which innovations may create new jobs (compensation effects). In addition, product and process innovations influence employment via different channels. The overall impact depends on a number of firm-, sector- as well as country-specific factors.

Keywords

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This chapter is largely based on Peters (2004). The model in section 3.4.1 and the international comparison in section 3.6 largely draws on a joint paper with Rupert Harrison, Jordi Jaumandreu, and Jacques Mairesse, see Harrison et al. (2005).

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References

33.

For a historical overview, see Petit (1995) or Freeman and Soete (1997).Google Scholar

34.

Closely related to the aspect of the shift in the labour demand from low-to high-skilled personnel is the increasing inequality of the relative wages across skill groups (see, e.g., Fitzenberger, 1999).Google Scholar

35.

Traditionally, patents have been used as an indicator to measure innovation output. However, patent-based indicators have been heavily criticised as being a poor measure of innovative outcome (see Griliches, 1990).Google Scholar

Hall, Lotti, and Mairesse (2006) modified the model by specifying two relationships between the observed price from the statistical office and firm-level prices for old and new products.Google Scholar

50.

See figures on the labour force development in Fachserie 1, Reihe 4.2.1 published by Statistisches Bundesamt (a) or Peters (2003). Moreover, one can observe an employment shift within the manufacturing as well as service sector to more knowledge-intensive branches; see Pfeiffer and Falk (1999).Google Scholar

52.

The DWH test is based on an artificial regression by including the predicted value of the endogenous right-hand-side variable (as a function of all exogenous variables) in a regression of the original model and applying an F test for significance of the additional regressor (see Davidson and MacKinnon, 1993). Using, for instance, the instruments proposed in regression (2) in Tables 3.7 and 3.8, the DWH statistic was 44.74 (p-value: 0.000) in manufacturing and 8.60 (0.003) in services; using the preferred instruments of regression (6), the corresponding figures were 4.46 (0.035) and 7.14 (0.008).Google Scholar

55.

See Hahn and Hausman (2002: 166–169) for the calculation of the test statistic.Google Scholar

56.

Hahn and Hausman (2002) suggest a sequential test procedure. If the null hypothesis of this test has been rejected, a similar specification test based on second-order unbiased Nagar-estimators should be carried out. If the second test has not led to a rejection of the null hypothesis, the limited-information maximum likelihood (LIML) estimator as the optimal combination of Nagar-estimators should be applied. If the second test has likewise failed, none of these estimators should be used at all.Google Scholar

57.

Using CIS 2 data covering the period 1994–1996, Falk (1999) showed that only market novelties have stimulated the expected labour demand. The expected employment change was an ordinal variable in the data set which required a different estimation method (ordered probit model). Furthermore, he used dummy variables for both kinds of product innovations. Replacing the continuous variables in eq. (3.16) with their dummy counterparts, however, did not alter the qualitative results.Google Scholar

60.

König, Licht, and Buscher (1995) found a significant positive effect of process innovations for the boom period 1990–1992 while Blechinger and Pfeiffer (1999) reported a significant negative effect for the recession period 1993–1995.Google Scholar

61.

According to Gibrat’s law, firms grow (in terms of employment or sales) proportionally and independently of their size, see Gibrat (1934). In contrast to that, Jovanovic (1982), for instance, stressed the importance of managerial efficiency and learning by doing and developed a model in which surviving young and small firms grow faster than older and larger ones.Google Scholar